MMSE denoisers correspond to 1-weakly convex regularizers via upper Moreau envelopes of negative log-marginals, enabling the first sublinear convergence rates for PnP proximal gradient descent.
Nonlinear total variation based noise removal algorithms,
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Reinforcement learning optimizes adaptive angle selection and dose allocation in sparse-view CT reconstruction, yielding better quality and defect detectability than uniform strategies under limited projections or dose.
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Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoisers
MMSE denoisers correspond to 1-weakly convex regularizers via upper Moreau envelopes of negative log-marginals, enabling the first sublinear convergence rates for PnP proximal gradient descent.
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Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction
Reinforcement learning optimizes adaptive angle selection and dose allocation in sparse-view CT reconstruction, yielding better quality and defect detectability than uniform strategies under limited projections or dose.